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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Exam Price:	USD $399
Exam Format:	Scenario-based Questions, Multiple Choice
Exam Duration:	150 minutes
Available Languages:	English, Spanish
Related Certifications:	ISACA Advanced in AI Security Management (AAISM)
Certificate Validity Period:	3 years
Passing Score:	450/800
Real Exam Qty:	90
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Online remote proctored exam or test center exam
Pre Condition:	Candidates must hold an active CISM or CISSP certification.
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
Topic 3	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
When using third-party pre-trained AI models for customer due diligence in a highly regulated financial industry, which of the following is the PRIMARY security concern?

A. Excessive model size requiring additional computing resources
B. Presence of unvalidated dependencies and typosquatting
C. Lower energy efficiency leading to higher operational costs
D. Inability to deploy model fully on-premises and vendor lock-in

Answer: B

Explanation:
Third-party pre-trained models often rely on external libraries and components that may include unvalidated or malicious dependencies. Risks such as typosquatting can introduce compromised packages, creating a direct supply chain security threat that can lead to system compromise or data exposure.

NEW QUESTION # 30
Which of the following should be done FIRST when identifying and analyzing entry points, interfaces, and components of AI systems that malicious actors could exploit?

A. Examine data flow diagrams.
B. Determine performance metrics.
C. Configure access controls.
D. Mitigate potential attack vectors.

Answer: A

Explanation:
Understanding the system structure and how data moves between components is the foundational step in identifying potential attack surfaces. Data flow diagrams provide a clear view of entry points, interfaces, and interactions, enabling effective analysis of where malicious actors could exploit the system.

NEW QUESTION # 31
During which phase of the AI life cycle are models assessed for security and determined to be free of malicious manipulations?

A. Testing
B. Evaluation
C. Verification
D. Validation

Answer: C

Explanation:
The verification phase focuses on ensuring the model has been built correctly and is free from tampering or malicious manipulation by checking integrity, security controls, and adherence to design specifications before deployment.

NEW QUESTION # 32
Which of the following is the GREATEST threat to an organization where shadow generative AI is prevalent?

A. Accidental leakage of proprietary data
B. Employee misuse of the AI system
C. Software licensing violations
D. Limited technical knowledge of AI operations

Answer: A

Explanation:
Shadow generative AI often operates outside governance and security controls, increasing the likelihood that sensitive or proprietary information is entered into uncontrolled systems, leading to unintended exposure or data leakage.

NEW QUESTION # 33
A medical imaging AI model is vulnerable to adversarial information that could cause misdiagnosis. Which control would BEST help detect such manipulations during inference?

A. Model tuning
B. Blue team testing
C. Red team testing
D. Anomaly detection

Answer: D

Explanation:
Anomaly detection during inference monitors inputs and outputs for unusual patterns that may indicate adversarial manipulation, enabling real-time detection of inputs designed to mislead the model.

NEW QUESTION # 34
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